AIMC Topic:
Computer Simulation

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Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks.

PloS one
Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes b...

Integrative learning for population of dynamic networks with covariates.

NeuroImage
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic ap...

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.

Genome biology
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-c...

Event-triggered adaptive neural networks control for fractional-order nonstrict-feedback nonlinear systems with unmodeled dynamics and input saturation.

Neural networks : the official journal of the International Neural Network Society
The event-triggered adaptive neural networks control is investigated in this paper for a class of fractional-order systems (FOSs) with unmodeled dynamics and input saturation. Firstly, in order to obtain an auxiliary signal and then avoid the state v...

Hierarchical progressive learning of cell identities in single-cell data.

Nature communications
Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resol...

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE.

Physics in medicine and biology
Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand...

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture.

Journal of chromatography. A
The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsol...

A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Scientific reports
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in ...

H synchronization of delayed neural networks via event-triggered dynamic output control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates H exponential synchronization (ES) of neural networks (NNs) with delay by designing an event-triggered dynamic output feedback controller (ETDOFC). The ETDOFC is flexible in practice since it is applicable to both full order a...

Low cost exoskeleton manipulator using bidirectional triboelectric sensors enhanced multiple degree of freedom sensory system.

Nature communications
Rapid developments of robotics and virtual reality technology are raising the requirements of more advanced human-machine interfaces for achieving efficient parallel control. Exoskeleton as an assistive wearable device, usually requires a huge cost a...